{"id":11702,"date":"2026-09-11T17:16:45","date_gmt":"2026-09-11T09:16:45","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11702"},"modified":"2026-09-11T20:03:50","modified_gmt":"2026-09-11T12:03:50","slug":"jase-202612-35-032","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-032","title":{"rendered":"An Expert-Guided Multi-Source Evidence Fusion Method for Beat-by-Beat ECG Arrhythmia Classification"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Hexiao Zhang<\/i><\/a><\/p>\n\n\n\n

School of Artificial Intelligence, Hebei University of Technology, Tianjin City, Tianjin, 300130, China<\/p>\n<\/div>\n\n\n\n

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Received: May 17, 2026
Accepted: August 14, 2026
Publication Date: September 11, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Architecture of the proposed ELBD-CDM framework. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.032<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Beat-level ECG classification is difficult because morphologically similar beats can cross AAMI boundaries, minority classes are sparse, and complementary cues are often fused implicitly. ELBD-CDM preserves waveform, clinician-semantic and morphological, and statistical rhythm evidence as separate streams. MSER constructs beat-level evidence, and WQ-CMF uses the waveform query embedding to assign sample-specific weights rather than fixed concatenation. Under the adopted MIT-BIH beat-level stratified protocol, ELBD-CDM achieved 99.24% Accuracy, 95.51% Macro-F1, and 99.25% Weighted-F1. F1 scores for S, F, and Q were 94.00%, 90.00%, and 95.00%, respectively. Removing MSER and WQ-CMF reduced Macro-F1 by 4.96 and 4.05 percentage points. Under the strongest waveform-branch Gaussian-noise setting (40 dB), ELBD-CDM retained 83.6% Macro-F1 and outperformed the reproduced comparators. The test set contained 460 S, 141 F, and 1,193 Q beats; therefore, Macro-F1 was emphasized over Accuracy. These results support sample-specific evidence fusion under this protocol, although inter-patient and external validation remain necessary.<\/p>\n\n\n\n

Keywords: ECG; arrhythmia; beat-level classification; multi-source evidence fusion; cross-modal fusion.<\/em><\/p>\n\n\n\n

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